Comparing The Forecasting Accuracy Metrics Of Support Vector Regression and ARIMA Algorithms For Non-Stationary Time Process

Abstract

Univariate time series forecasting is a crucial machine learning issue across many fields notably sentiment analysis, economy, medicine, agriculture, and finance. In this working paper, we tackled comparing the Support Vector Regression (SVR) to the traditional Autoregressive Integrated Moving Average (ARIMA) algorithms in terms of forecasting through a real case study. In fact, the data set used in this investigation has been extracted from the World Bank. The target time series is the American Foreign direct investment, net outflows (% of GDP) which includes the data for 50 years from 1972 to 2021. For analytical and comparison purposes, all the compilations have been done using the R programming language for Windows 10. The statistical findings revealed that, in short-term prediction, the forecast accuracy of both algorithms reduces in terms of error accuracy, significantly. Comparatively, the analysis conducted in this investigation demonstrates that the machine learning algorithms, especially the SVM one perform better than the ARIMA in short-term forecasting since its accuracy functions are the lowest. Thus, we highly recommend future research to compare the advanced machine learning algorithms especially the recurrent neural network algorithms with the classical algorithms, especially with the ARIMA approach in order to choose the best algorithm in terms of results and predictive performance.